{"id":"W4361215960","doi":"10.1371/journal.pdig.0000208","title":"Developing better digital health measures of Parkinson’s disease using free living data and a crowdsourced data analysis challenge","year":2023,"lang":"en","type":"article","venue":"PLOS Digital Health","topic":"Parkinson's Disease Mechanisms and Treatments","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Human Genome Research Institute; National Institutes of Health; National Institute of Neurological Disorders and Stroke; Sanofi; Merck KGaA; Icahn School of Medicine at Mount Sinai; Eli Lilly and Company; National Center for Advancing Translational Sciences; Michael J. Fox Foundation for Parkinson's Research","keywords":"Benchmarking; Leverage (statistics); Data collection; Dyskinesia; Medicine; Disease; Citizen science; Digital health; Real world data; Parkinson's disease; Confounding; Data science; Polychoric correlation; Psychology; Computer science; Machine learning; Health care; Statistics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.00994835,0.001717063,0.001092505,0.00272123,0.0009825068,0.002915294,0.001756913,0.002332979,0.001748788],"category_scores_gemma":[0.02853165,0.0003447689,0.001572776,0.00216361,0.001038679,0.002714287,0.004169714,0.001789321,0.001363992],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001228652,"about_ca_system_score_gemma":0.001671185,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008515175,"about_ca_topic_score_gemma":0.01134302,"domain_scores_codex":[0.992009,0.004392092,0.0005082482,0.001768574,0.001078959,0.0002430546],"domain_scores_gemma":[0.9820116,0.00871334,0.001329551,0.004899086,0.002163768,0.0008827762],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001486128,0.001706546,0.333589,0.003008642,0.002287235,0.001057163,0.003820272,0.08247484,0.007717648,0.007645822,0.1250565,0.4301502],"study_design_scores_gemma":[0.0004785529,0.001290152,0.2536305,0.001668954,0.000613536,0.001385402,0.009556712,0.4757465,0.01520347,0.08778508,0.1521474,0.0004937772],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6166319,0.006993581,0.2256303,0.01685885,0.002509993,0.002820073,0.09861904,0.009073994,0.02086233],"genre_scores_gemma":[0.7936223,0.0007991196,0.1337808,0.001919679,0.0004159056,0.001109786,0.06559228,0.0003483494,0.00241183],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9900516,"threshold_uncertainty_score":0.0526126,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.204904671327659,"score_gpt":0.3579831517499391,"score_spread":0.1530784804222801,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}